SoroushVahidi/scidocs huggingface dataset:
Multi-Sensor Data Integration Using Deep Learning for Characterization of Defects in Steel Elements †
(2018)

Nowadays, there is a strong demand for inspection systems integrating both high sensitivity under various testing conditions and advanced processing allowing automatic identification of the examined object state and detection of threats. This paper presents the possibility of utilization of a magnetic multi-sensor matrix transducer for characterization of defected areas in steel elements and a deep learning based algorithm for integration of data and final identification of the object state. The transducer allows sensing of a magnetic vector in a single location in different directions. Thus, it enables detecting and characterizing any material changes that affect magnetic properties regardless of their orientation in reference to the scanning direction. To assess the general application capability of the system, steel elements with rectangular-shaped artificial defects were used. First, a database was constructed considering numerical and measurements results. A finite element method was used to run a simulation process and provide transducer signal patterns for different defect arrangements. Next, the algorithm integrating responses of the transducer collected in a single position was applied, and a convolutional neural network was used for implementation of the material state evaluation model. Then, validation of the obtained model was carried out. In this paper, the procedure for updating the evaluated local state, referring to the neighboring area results, is presented. Finally, the results and future perspective are discussed.
Direct visibility of point sets
(2007)

This paper proposes a simple and fast operator, the "Hidden" Point Removal operator, which determines the visible points in a point cloud, as viewed from a given viewpoint. Visibility is determined without reconstructing a surface or estimating normals. It is shown that extracting the points that reside on the convex hull of a transformed point cloud, amounts to determining the visible points. This operator is general - it can be applied to point clouds at various dimensions, on both sparse and dense point clouds, and on viewpoints internal as well as external to the cloud. It is demonstrated that the operator is useful in visualizing point clouds, in view-dependent reconstruction and in shadow casting.
Better to be frustrated than bored: The incidence, persistence, and impact of learners' cognitive-affective states during interactions with three different computer-based learning environments
(2010)

We study the incidence (rate of occurrence), persistence (rate of reoccurrence immediately after occurrence), and impact (effect on behavior) of students’ cognitive-affective states during their use of three different computer-based learning environments. Students’ cognitive-affective states are studied using different populations (Philippines, USA), different methods (quantitative field observation, self-report), and different types of learning environments (dialogue tutor, problemsolving game, and problem-solving based Intelligent Tutoring System). By varying the studies along these multiple factors, we can have greater confidence that findings which generalize across studies are robust. The incidence, persistence, and impact of boredom, frustration, confusion, engaged concentration, delight, and surprise were compared. We found that boredom was very persistent across learning environments and was associated with poorer learning and problem behaviors, such as gaming the system. Despite prior hypothesis to the contrary, frustration was less persistent, less associated with poorer learning, and did not appear to be an antecedent to gaming the system. Confusion and engaged concentration were the most common states within all three learning environments. Experiences of delight and surprise were rare. These findings suggest that significant effort should be put into detecting and responding to boredom and confusion, with a particular emphasis on developing pedagogical interventions to disrupt the “vicious cycles” which occur when a student becomes bored and remains bored for long periods of time.
Blood Exosomes Endowed with Magnetic and Targeting Properties for Cancer Therapy.
(2016)

Exosomes are a class of naturally occurring nanoparticles that are secreted endogenously by mammalian cells. Clinical applications for exosomes remain a challenge because of their unsuitable donors, low scalability, and insufficient targeting ability. In this study, we developed a dual-functional exosome-based superparamagnetic nanoparticle cluster as a targeted drug delivery vehicle for cancer therapy. The resulting exosome-based drug delivery vehicle exhibits superparamagnetic behavior at room temperature, with a stronger response to an external magnetic field than individual superparamagnetic nanoparticles. These properties enable exosomes to be separated from the blood and to target diseased cells. In vivo studies using murine hepatoma 22 subcutaneous cancer cells showed that drug-loaded exosome-based vehicle delivery enhanced cancer targeting under an external magnetic field and suppressed tumor growth. Our developments overcome major barriers to the utility of exosomes for cancer application.
Large-scale concept ontology for multimedia
(2006)

As increasingly powerful techniques emerge for machine tagging multimedia content, it becomes ever more important to standardize the underlying vocabularies. Doing so provides interoperability and lets the multimedia community focus ongoing research on a well-defined set of semantics. This paper describes a collaborative effort of multimedia researchers, library scientists, and end users to develop a large standardized taxonomy for describing broadcast news video. The large-scale concept ontology for multimedia (LSCOM) is the first of its kind designed to simultaneously optimize utility to facilitate end-user access, cover a large semantic space, make automated extraction feasible, and increase observability in diverse broadcast news video data sets
A droop control strategy of parallel-inverter-based microgrid
(2011)

In this paper, the control strategy for a parallel-inverters-based microgrid is presented. The control strategy includes outer active and reactive power control loop based on frequency and voltage droop method to avoid critical communications among distributed generation units (DGs). The inner inverter control loop is employed to produce the required inverter output voltage provided by the outer power loop. In addition, two inverter control schemes are introduced and compared. This microgrid can operate at both grid-connected and islanded mode with proper power sharing capability between parallel DG systems. Moreover, smooth transition between operating modes is achieved without causing negative effect on the utility and critical loads. The performance of this control strategy is verified in simulation using Matlab/Simulink.
Human EEG responses to 1–100 Hz flicker: resonance phenomena in visual cortex and their potential correlation to cognitive phenomena
(2001)

The individual properties of visual objects, like form or color, are represented in different areas in our visual cortex. In order to perceive one coherent object, its features have to be bound together. This was found to be achieved in cat and monkey brains by temporal correlation of the firing rates of neurons which code the same object. This firing rate is predominantly observed in the gamma frequency range (approx. 30–80 Hz, mainly around 40 Hz). In addition, it has been shown in humans that stimuli which flicker at gamma frequencies are processed faster by our brains than when they flicker at different frequencies. These effects could be due to neural oscillators, which preferably oscillate at certain frequencies, so-called resonance frequencies. It is also known that neurons in visual cortex respond to flickering stimuli at the frequency of the flickering light. If neural oscillators exist with resonance frequencies, they should respond more strongly to stimulation with their resonance frequency. We performed an experiment, where ten human subjects were presented flickering light at frequencies from 1 to 100 Hz in 1-Hz steps. The event-related potentials exhibited steady-state oscillations at all frequencies up to at least 90 Hz. Interestingly, the steady-state potentials exhibited clear resonance phenomena around 10, 20, 40 and 80 Hz. This could be a potential neural basis for gamma oscillations in binding experiments. The pattern of results resembles that of multiunit activity and local field potentials in cat visual cortex.
Tribeca: A Stream Database Manager for Network Traffic Analysis
(1996)

High speed computer and telephone networks carry large amounts of data and signalling traffic. The engineers who build and maintain these networks use a combination of hardware and software tools to monitor the stream of network traffic. Some of these tools operate directly on the live network; others record data on magnetic tape for later offline analysis by software. Most analysis tasks require tens to hundreds of gigabytes of data. Traffic analysis applications include protocol performance analysis, conformance testing, error monitoring and fraud detection.
Sliding-Window Superposition Coding: Two-User Interference Channels
(2017)

A low-complexity coding scheme is developed to achieve the r at egion of maximum likelihood decoding for interference channels. As in the classical rate-splitting mu ltiple access scheme by Grant, Rimoldi, Urbanke, and Whitin g, the proposed coding scheme uses superposition of multiple c od words with successive cancellation decoding, which can be implemented using standard point-to-point encoders and decoders. Unlike rate-splitting multiple access, which is not rate-optimal for multiple receivers, the proposed coding s cheme transmits codewords over multiple blocks in a stagger ed manner and recovers them successively over sliding decodin g wi dows, achieving the single-stream optimal rate region as well as the more general Han–Kobayashi inner bound for the tw o-user interference channel. The feasibility of this schem e in practice is verified by implementing it using commercial c hannel codes over the two-user Gaussian interference chann el. I. I NTRODUCTION For high data rates and massive connectivity, next-generat ion cellular networks are expected to deploy many small base stations. While such dense deployment provides the ben fit of bringing radio closer to end users, it also increases the amount of interference from neighboring cells. Consequ ently, efficient and effective management of interference is expected to become one of the main challenges for high-spe ctral-efficiency, low-power, broad-coverage wireless communications. Over the past few decades, several techniques at different p otocol layers [1]–[3] have been proposed to mitigate adver se effects of interference in wireless networks. One importan t conceptual technique at the physical layer is simultaneous decoding[4, Section 6.2], [5]. In this decoding method, each receive r attempts to recover both the intended and a subset of the interfering codewords at the same time. When the inter fer nce is strong [6], [7] and weak [8]–[11], simultaneous The material in this paper was presented in part in the IEEE In ternational Symposium on Information Theory (ISIT) 2014, H onolulu, HI, and in part in the IEEE Globecom Workshops (GC Wkshps) 2014, Austin , TX. L. Wang is jointly with the Department of Electrical Enginee ring, Stanford University, Stanford, CA 94305 USA and the De partment of Electrical Engineering Systems, Tel Aviv University, Tel Aviv, Israe l ( mail: wanglele@stanford.edu). Y.-H. Kim is with the Department of Electrical and Computer E ngineering, University of California, San Diego, La Jolla, CA 92093 USA (e-mail: yhk@ucsd.edu). C.-Y. Chen is with Broadcom Limited, 190 Mathilda Place, Sun nyvale, CA 94086 USA (email: uscychen@gmail.com). H. Park is with the School of Electronics and Computer Engine eri g, Chonnam National University, Gwangju 61186, Korea ( e-mail: hpark1@jnu.ac.kr). E. Şaşoğlu is with Intel Corporation, Santa Clara, CA 950 54 USA (e-mail: eren.sasoglu@gmail.com).
A brain-controlled exoskeleton with cascaded event-related desynchronization classifiers
(2017)

This paper describes a brain-machine interface for the online control of a powered lower-limb exoskeleton based on electroencephalogram (EEG) signals recorded over the user’s sensorimotor cortical areas. We train a binary decoder that can distinguish two different mental states, which is applied in a cascaded manner to efficiently control the exoskeleton in three different directions: walk front, turn left and turn right. This is realized by first classifying the user’s intention to walk front or change the direction. If the user decides to change the direction, a subsequent classification is performed to decide turn left or right. The user’s mental command is conditionally executed considering the possibility of obstacle collision. All five subjects were able to successfully complete the 3-way navigation task using brain signals while mounted in the exoskeleton. We observed on average 10.2% decrease in overall task completion time compared to the baseline protocol. August 25, 2016 DRAFT
Microsaccades uncover the orientation of covert attention
(2003)

Fixational eye movements are subdivided into tremor, drift, and microsaccades. All three types of miniature eye movements generate small random displacements of the retinal image when viewing a stationary scene. Here we investigate the modulation of microsaccades by shifts of covert attention in a classical spatial cueing paradigm. First, we replicate the suppression of microsaccades with a minimum rate about 150 ms after cue onset. Second, as a new finding we observe microsaccadic enhancement with a maximum rate about 350 ms after presentation of the cue. Third, we find a modulation of the orientation towards the cue direction. These multiple influences of visual attention on microsaccades accentuate their role for visual information processing. Furthermore, our results suggest that microsaccades can be used to map the orientation of visual attention in psychophysical experiments.
Document-level Multi-aspect Sentiment Classification by Jointly Modeling Users, Aspects, and Overall Ratings
(2018)

Document-level multi-aspect sentiment classification aims to predict user’s sentiment polarities for different aspects of a product in a review. Existing approaches mainly focus on text information. However, the authors (i.e. users) and overall ratings of reviews are ignored, both of which are proved to be significant on interpreting the sentiments of different aspects in this paper. Therefore, we propose a model called Hierarchical User Aspect Rating Network (HUARN) to consider user preference and overall ratings jointly. Specifically, HUARN adopts a hierarchical architecture to encode word, sentence, and document level information. Then, user attention and aspect attention are introduced into building sentence and document level representation. The document representation is combined with user and overall rating information to predict aspect ratings of a review. Diverse aspects are treated differently and a multi-task framework is adopted. Empirical results on two real-world datasets show that HUARN achieves state-of-the-art performances.
Pixel Based Off-line Signature Verification System
(2015)

The verification of handwritten signatures is one of the oldest and the most popular authentication methods all around the world. As technology improved, different ways of comparing and analyzing signatures become more and more sophisticated. Since the early seventies, people have been exploring how computers can fully take over the task of signature verification and tried different methods. However, none of them is satisfactory enough and time consuming too. Therefore, our proposed pixel based offline signature verification system is one of the fastest and easiest ways to authenticate any handwritten signature we have ever found. For signature acquisition, we have used scanner. Then we have divided the signature image into 2D array and calculated the hexadecimal RGB value of each pixel. After that, we have calculated the total percentage of matching. If the percentage of matching is more than 90, the signature is considered as valid otherwise invalid. We have experimented on more than 35 signatures and the result of our experiment is quite impressive. We have made the whole system web based so that the signature can be verified from anywhere. The average execution time for signature verification is only 0.00003545 second only. Keywords– off-line signature, signature verification, pixel, execution time, pre-processing
Norms for word lists that create false memories.
(1999)

Roediger and McDermott (1995) induced false recall and false recognition for words that were not presented in lists. They had subjects study 24 lists of 15 words that were associates of a common word (called the critical target or critical lure) that was not presented in the list. False recall and false recognition of the critical target occurred frequently in response to these lists. The purpose of the current work was to provide a set of normative data for the lists Roediger and McDermott used and for 12 others developed more recently. We tested false recall and false recognition for critical targets from 36 lists. Despite the fact that all lists were constructed to produce false remembering, the diversity in their effectiveness was large--60% or more of subjects falsely recalled window and sleep following the appropriate lists, and false recognition for these items was greater than 80%. However, the list generated from king led to 10% false recall and 27% false recognition. Possible reasons for these wide differences in effectiveness of the lists are discussed. These norms serve as a useful benchmark for designing experiments about false recall and false recognition in this paradigm.
Software defect prediction using machine learning on test and source code metrics
(2014)

Context. Software testing is the process of finding faults in software while executing it. The results of the testing are used to find and correct faults. Software defect prediction estimates where faults are likely to occur in source code. The results from the defect prediction can be used to optimize testing and ultimately improve software quality. Machine learning, that concerns computer programs learning from data, is used to build prediction models which then can be used to classify data. Objectives. In this study we, in collaboration with Ericsson, investigated whether software metrics from source code files combined with metrics from their respective tests predicts faults with better prediction performance compared to using only metrics from the source code files. Methods. A literature review was conducted to identify inputs for an experiment. The experiment was applied on one repository from Ericsson to identify the best performing set of metrics. Results. The prediction performance results of three metric sets are presented and compared with each other. Wilcoxon’s signed rank tests are performed on four different performance measures for each metric set and each machine learning algorithm to demonstrate significant differences of the results. Conclusions. We conclude that metrics from tests can be used to predict faults. However, the combination of source code metrics and test metrics do not outperform using only source code metrics. Moreover, we conclude that models built with metrics from the test metric set with minimal information of the source code can in fact predict faults in the source code.
Core stability exercise principles.
(2008)

Core stability is essential for proper load balance within the spine, pelvis, and kinetic chain. The so-called core is the group of trunk muscles that surround the spine and abdominal viscera. Abdominal, gluteal, hip girdle, paraspinal, and other muscles work in concert to provide spinal stability. Core stability and its motor control have been shown to be imperative for initiation of functional limb movements, as needed in athletics. Sports medicine practitioners use core strengthening techniques to improve performance and prevent injury. Core strengthening, often called lumbar stabilization, also has been used as a therapeutic exercise treatment regimen for low back pain conditions. This article summarizes the anatomy of the core, the progression of core strengthening, the available evidence for its theoretical construct, and its efficacy in musculoskeletal conditions.
An Extension on “ Statistical Comparisons of Classifiers over Multiple Data Sets ” for all Pairwise Comparisons
(2018)

In a recently published paper in JMLR, Demšar (2006) recommends a set of non-parametric statistical tests and procedures which can be safely used for comparing the performance of classifiers over multiple data sets. After studying the paper, we realize that the paper correctly introduces the basic procedures and some of the most advanced ones when comparing a control method. However, it does not deal with some advanced topics in depth. Regarding these topics, we focus on more powerful proposals of statistical procedures for comparing n×n classifiers. Moreover, we illustrate an easy way of obtaining adjusted and comparable p-values in multiple comparison procedures.
KEEL: a software tool to assess evolutionary algorithms for data mining problems
(2008)

This paper introduces a software tool named KEEL, which is a software tool to assess evolutionary algorithms for Data Mining problems of various kinds including as regression, classification, unsupervised learning, etc. It includes evolutionary learning algorithms based on different approaches: Pittsburgh, Michigan and IRL, as well as the integration of evolutionary learning techniques with different pre-processing techniques, allowing it to perform a complete analysis of any learning model in comparison to existing software tools. Moreover, KEEL has been designed with a double goal: research and educational.
Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients
(2004)

This paper introduces a novel parameter automation strategy for the particle swarm algorithm and two further extensions to improve its performance after a predefined number of generations. Initially, to efficiently control the local search and convergence to the global optimum solution, time-varying acceleration coefficients (TVAC) are introduced in addition to the time-varying inertia weight factor in particle swarm optimization (PSO). From the basis of TVAC, two new strategies are discussed to improve the performance of the PSO. First, the concept of "mutation" is introduced to the particle swarm optimization along with TVAC (MPSO-TVAC), by adding a small perturbation to a randomly selected modulus of the velocity vector of a random particle by predefined probability. Second, we introduce a novel particle swarm concept "self-organizing hierarchical particle swarm optimizer with TVAC (HPSO-TVAC)". Under this method, only the "social" part and the "cognitive" part of the particle swarm strategy are considered to estimate the new velocity of each particle and particles are reinitialized whenever they are stagnated in the search space. In addition, to overcome the difficulties of selecting an appropriate mutation step size for different problems, a time-varying mutation step size was introduced. Further, for most of the benchmarks, mutation probability is found to be insensitive to the performance of MPSO-TVAC method. On the other hand, the effect of reinitialization velocity on the performance of HPSO-TVAC method is also observed. Time-varying reinitialization step size is found to be an efficient parameter optimization strategy for HPSO-TVAC method. The HPSO-TVAC strategy outperformed all the methods considered in this investigation for most of the functions. Furthermore, it has also been observed that both the MPSO and HPSO strategies perform poorly when the acceleration coefficients are fixed at two.